Due to the increasing demand for wireless communication, it has become necessary to develop techniques for more efficiently using the allocated frequency bands, i.e. increasing the capacity to communicate information within a limited available bandwidth. In conventional low capacity wireless communication systems, information is transmitted from a base station to subscribers by broadcasting omnidirectional signals on one of several predetermined frequency channels. Similarly, the subscribers transmit information back to the base station by broadcasting similar signals on one of the frequency channels. In this system, multiple users independently access the system through the division of the frequency band into distinct subband frequency channels. This technique is known as frequency division multiple access (FDMA).
A standard technique used by commercial wireless phone systems to increasing capacity is to divide the service region into spatial cells. Instead of using just one base station to serve all users in the region, a collection of base stations are used to independently service separate spatial cells. In such a cellular system, multiple users can reuse the same frequency channel without interfering with each other, provided they access the system from different spatial cells. The cellular concept, therefore, is a simple type of spatial division multiple access (SDMA).
In the case of digital communication, additional techniques can be used to increase capacity. A few well known examples are time division multiple access (TDMA) and code division multiple access (CDMA). TDMA allows several users to share a single frequency channel by assigning their data to distinct time slots. CDMA is normally a spread-spectrum technique that does not limit individual signals to narrow frequency channels but spreads them throughout the frequency spectrum of the entire band. Signals sharing the band are distinguished by assigning them different orthogonal digital code sequences. CDMA has been considered the most promising method among the various air-interfaces in the industry, as shown by theoretical analysis (See, for example, Andrew J. Viterbi, CDMA Principles of Spread Spectrum Communications, and Vijay K. Garg et al., Applications of CDMA in Wireless/Personal Communications.
Despite the promise of CDMA, practical issues such as power control speed and inter-base station interference considerably limited system effectiveness in its initial phase of implementation. CDMA based system capacity depends very much on the ability to provide for very accurate power control; but in a mobile environment, the signal may fluctuate too fast for the system to control. Unfortunately, mobile wireless environments are often characterized by unstable signal propagation, severe signal attenuation between the communicating entities and co-channel interference by other radio sources. Moreover, many urban environments contain a significant number of reflectors (such as buildings), causing a signal to follow multiple paths from the transmitter to the receiver. Because the separate parts of such a multipath signal can arrive with different phases that destructively interfere, multipath can result in unpredictable signal fading. In addition, in order to provide service to shadowed areas, radiated power is increased, thereby increasing inter base station interference and degrading system performance significantly.
Recently, considerable attention has focused on ways to increase wireless system performance by further exploiting the spatial domain. It is well recognized that SDMA techniques, in principle, could significantly improve the CDMA based network performance. These techniques have varying degrees of sophistication and complexity. Currently proposed approaches are either simple but not very effective or effective but too complex for practical implementation.
One well-known SDMA technique is to provide the base station with a set of independently controlled directional antennas, thereby dividing the cell into separate sectors, each controlled by a separate antenna. As a result, the frequency reuse in the system can be increased and/or cochannel interference can be reduced. Instead of independently controlled directional antennas, this technique can also be implemented with a coherently controlled antenna array. Using a signal processor to control the relative phases of the signals applied to the antenna elements, predetermined beams can be formed in the directions of the separate sectors. Similar signal processing can be used to selectively receive signals only from within the distinct sectors. These simple sectoring techniques, however, only provide a relatively small increase in capacity.
U.S. Pat. No. 5,563,610 discloses a method for mitigating signal fading due to multipath in a CDMA system. By introducing intentional delays into received signals, non-correlated fading signal components can be better differentiated by the RAKE receiver. Although this diversity method can reduce the effects of fading, it does not take advantage of the spatial domain and does not directly increase system capacity. Moreover, this approach, which combines angular and time diversity using a fixed beams configuration, is not effective since either the beams outputs are significantly different in level or they are similar in level but highly correlated. If two signal parts are arriving from similar direction, they are passing through one beam and thus are non differentiable. If the signal parts are arriving between beams, on the other hand, the levels are similar but the they are well correlated.
More sophisticated SDMA techniques have been proposed that could dramatically increase system capacity. For example, U.S. Pat. No. 5,471,647 and U.S. Pat. No. 5,634,199, both to Gerlach et al., and U.S. Pat. No. 5,592,490 to Barratt et al. disclose wireless communication systems that increase performance by exploiting the spatial domain. In the downlink, the base station determines the spatial channel of each subscriber and uses this channel information to adaptively control its antenna array to form customized narrow beams. These beams transmit an information signal over multiple paths so that the signal arrives to the subscriber with maximum strength. The beams can also be selected to direct nulls to other subscribers so that cochannel interference is reduced. In the uplink, the base station uses the channel information to spatially filter the received signals so that the uplink signal is received with maximum sensitivity and distinguished from the signals transmitted by other subscribers. Through selective power delivery by intelligent directional beams, the inter base station interference and the carrier to interference ratio at the base station receivers can be reduced.
The biggest issue in adaptive beamforming is how to quickly estimate the wireless air channel to allow for effective beams allocation. In the uplink, there are known signal processing techniques for estimating the spatial channel from the signals received at the base station antenna array. These techniques conventionally involve an inversion or singular value decomposition of a signal covariance matrix. The computational complexity of this calculation, however, is so high that it is presently not practical to implement. These highly complex approaches capitalize on the theory of array signal processing. This approach estimates the uplink channel (e.g. the angles and times of arrival of the multipath signal parts) to create a space-time matched filter to allow for maximum signal delivery. The proposed method involves computation of a signal covariance matrix and derivation of its eigenvectors to determine the array coefficients. The basic problem of array signal processing is formulated in the following expression: EQU X=AS+N
where X is a matrix of antenna array signal snapshots (each column incorporates snapshots of all antenna elements), S is the transmitted signal matrix (each column incorporates snapshots of the information signal, A is the antenna array & channel response matrix, and N is the noise matrix. The main challenge of array signal processing is to estimate S based on the statistics of A and S, that is, to reliably and correctly estimate all the incoming signals with presence of interference and thermal noise, N. This problem has been a subject for extensive research for several years. Two well known estimating algorithms involve Maximum Likelihood Sequence Estimation (MLSE) and Minimum Mean Square Error (MMSE). Using these techniques, if S represents signals with known properties such as constant modules (CM), or finite alphabet (FA), the process can be executed using the known signal's temporal structure statistics. If the array manifold is known, than convergence can be made faster. This process, however, is very computational intensive. In a base station that is required to simultaneously support more than 100 mobile units, the computation power is presently beyond practical realization.
Most adaptive beam forming methods described in the art (e.g. U.S. Pat. No. 5,434,578) deal extensively with uplink estimation, while requiring extensive computation resources. Few, however, deal with downlink estimation, which is a more difficult problem. Because the spatial channel is frequency dependent and the uplink and downlink frequencies are often different, the uplink beamforming techniques do not provide the base station with sufficient information to derive the downlink spatial channel information and improve system capacity. One technique for obtaining downlink channel information is to use feedback from the subscriber. The required feedback rates, however, make this approach impractical to implement.
There is a need, therefore, for significantly increasing wireless system capacity using beamforming methods that overcome the limitations in the known approaches.